I am not so strong with Time Series so I thought I'd ask here.
I am working on a Bayesian Graphical Model where I have observations recorded once a day. So obviously, there is some influence of time ...

This question is about data visualization and statistical graphics. I have been trying to present statistical data in map. The data is at county level in the US and also at time state level. My data ...

This question is essentially same as this one. The question is: How do you calculate conditional probability of a node in Bayesian network when it has a continuous node as a parent? However, I cannot ...

assume we have a standard Kalman filter with input controls, following wikipedia notation (http://en.wikipedia.org/wiki/Kalman_filter) where the latent state is $x_{t}$ and the observation is $z_{t}$, ...

Given $n$ discrete random variables $X_1,...,X_n$, a distribution $p$ on $X=(X_1,...,X_d)$ and a DAG (Directed Acyclic Graph) $G$ on $\{1,...,d\}$, which is the distribution $q$ factorizing with $G$ ...

In Chris Bishop PRML book p.465 equation 10.6, the derivation doesn't explain why exactly the term $\int q_j ln(q_j) dz_j $ was generated, is not that term supposed to be multiplied by constant, did ...

To learn similarities/differences between different instances (that are in the form of tree), what are the suitable methods/approaches?
I know kernel methods and particularly tree kernels, but would ...

My goal is to find closed form equations for posterior marginals $P(x_n|y_0, ... , y_n)$ in a general HMM.
I was told that we can calculate it exactly via BP (belief propagation, thought not sure how ...

I am looking to implement statistical relational learning, preferably in a modern programming language, and came across Factorie and Figaro for Scala. But most resources online that compare these are ...

In a graphical model with variables with continuous distriubtions, and some observed variables, how can I compute the messages to be passed?
I know the messages but I don't know how to implement it?
...

When chordal graphs are used to model probability distributions, why is it that they do not lose conditional independences when its transformed from a undirected to a directed to a factor graph and ...

I was trying to form an example where I had 3 r.v.s such that the distribution describing them had more conditional independencies or independencies than the directed graphical model corresponding to ...

I am performing a multiple linear regression and I have a plot of the my first two explanatory variables vs the residuals and also a plot with the residuals vs the fitted values. I am not quite sure ...

Is there a formal treatment of the role/power of latent/hidden variables in graphical models and other machine learning models (e.g., structural equation models)?
For example, the Restricted Boltzman ...

I am looking for a research paper that basically describes a hidden markov model that has multiple observations, and some observations that have conditional dependencies. For example, please consider ...

I have been trying to understand EM and I am having a hard time understanding what a latent variable is. In particular, I am having issues in identifying whether in a particular model that I am using, ...

What are the advantages of log linear representation in opposite of table representation? Is it simply computational issue ( avoid overflowing)?
For example, in a markov network A-B we can represent ...